SHEF-LIUM-NN: Sentence-level Quality Estimation with Neural Network Features

Abstract

This paper describes our systems for Task 1 of the WMT16 Shared Task on Quality Estimation. Our submissions use (i) a continuous space language model (CSLM) to extract sentence embeddings and cross-entropy scores, (ii) a neural network machine translation (NMT) model, (iii) a set of QuEst features, and (iv) a combination of features produced by QuEst and with CSLM and NMT. Our primary submission achieved third place in the scoring task and second place in the ranking task. Another interesting finding is the good performance obtained from using as features only CSLM sentence embeddings, which are learned in an unsupervised fashion without any additional handcrafted features.